The field of catalytic green hydrogen production, although indispensable for the transition to a renewable future, still suffers from widespread irreproducibility of results that limits its full commercialisation. The largest obstacle lies in the methods widely employed in synthesising the active catalysts, which impede unambiguously identifying the property-structure...
Understanding, predicting, and discovering the properties and performance of materials is key to delivering the technologies that power our economy and allow for the sustainable development of our society. For this reason, materials simulations have become one of the most intensive and fast-growing domains for high-performance computing worldwide, with a recognized European...
Predicting novel materials with specific desirable properties is a major aim of ab initio computational materials science (aiCMS) and an urgent requirement of basic and applied materials science, engineering and industry. Such materials can have immense impact on the environment and on society, e.g. on energy, transport, IT, medical-device sectors and much more. Currently,...
The main goal of MaX is to allow the pre-exascale and exascale computers expected in Europe in the 2020s to meet the demands of a large and growing base of researchers committed to materials discovery and design. This goal will be achieved through:
- an innovative software development model, based on the concept of separation of concerns, that will enable...
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